47 research outputs found
Recommended from our members
Incremental Learning with Large Datasets
This dissertation focuses on the novel learning strategy based on geometric support vector machines to address the difficulties of processing immense data set. Support vector machines find the hyper-plane that maximizes the margin between two classes, and the decision boundary is represented with a few training samples it becomes a favorable choice for incremental learning. The dissertation presents a novel method Geometric Incremental Support Vector Machines (GISVMs) to address both efficiency and accuracy issues in handling massive data sets. In GISVM, skin of convex hulls is defined and an efficient method is designed to find the best skin approximation given available examples. The set of extreme points are found by recursively searching along the direction defined by a pair of known extreme points. By identifying the skin of the convex hulls, the incremental learning will only employ a much smaller number of samples with comparable or even better accuracy. When additional samples are provided, they will be used together with the skin of the convex hull constructed from previous dataset. This results in a small number of instances used in incremental steps of the training process. Based on the experimental results with synthetic data sets, public benchmark data sets from UCI and endoscopy videos, it is evident that the GISVM achieved satisfactory classifiers that closely model the underlying data distribution. GISVM improves the performance in sensitivity in the incremental steps, significantly reduced the demand for memory space, and demonstrates the ability of recovery from temporary performance degradation
Online Metric-Weighted Linear Representations for Robust Visual Tracking
In this paper, we propose a visual tracker based on a metric-weighted linear
representation of appearance. In order to capture the interdependence of
different feature dimensions, we develop two online distance metric learning
methods using proximity comparison information and structured output learning.
The learned metric is then incorporated into a linear representation of
appearance.
We show that online distance metric learning significantly improves the
robustness of the tracker, especially on those sequences exhibiting drastic
appearance changes. In order to bound growth in the number of training samples,
we design a time-weighted reservoir sampling method.
Moreover, we enable our tracker to automatically perform object
identification during the process of object tracking, by introducing a
collection of static template samples belonging to several object classes of
interest. Object identification results for an entire video sequence are
achieved by systematically combining the tracking information and visual
recognition at each frame. Experimental results on challenging video sequences
demonstrate the effectiveness of the method for both inter-frame tracking and
object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and
Machine Intelligenc
Recommended from our members
The use of silent substitution in measuring isolated cone- & rod- human electroretinograms. An electrophysiological study of human rod- and cone- photoreceptor activity derived using silent substitution paradigm
After over a decade of its discovery, the Electroretinogram (ERG) still remains the objective tool that is conventionally used in assessment of retinal function in health and disease. Although there is ongoing research in developing ERG- recording techniques, interpretation and clinical applications, there is still a limited understanding on how each photoreceptor class contribute to the ERG waveform and their role and/or susceptibilities in various retinal diseases still remains unclear. Another limitation with currently used conventional testing protocols in a clinical setting is the requirement of an adaptation period which is time-consuming. Furthermore, the ERG responses derived in this manner are recorded under different stimulus conditions, thus, making comparison of these signals difficult. To address these issues and develop a new testing method, we employed silent substitution paradigm in obtaining cone- and rod- isolating ERGs using sine- and square- wave temporal profiles. The ERGs achieved in this manner were shown to be photoreceptor-selective. Furthermore, these responses did not only provide the functional index of photoreceptors but their contributions to their successive postreceptoral pathways. We believe that the substitution stimuli used in this thesis could be a valuable tool in functional assessment of individual photoreceptor classes in normal and pathological conditions. Furthermore, we speculate that this method of cone/rod activity isolation could possibly be used in developing faster and efficient photoreceptor-selective testing protocols without the need of adaptation.Bradford School of Optometry and Vision Science
Recommended from our members
The use of Silent Substitution in measuring isolated cone- and rod- Human ERGs
After over a decade of its discovery, the Electroretinogram (ERG) still remains
the objective tool that is conventionally used in assessment of retinal function in
health and disease. Although there is ongoing research in developing ERG recording techniques, interpretation and clinical applications, there is still a limited
understanding on how each photoreceptor class contribute to the ERG waveform
and their role and/or susceptibilities in various retinal diseases still remains
unclear. Another limitation with currently used conventional testing protocols in a
clinical setting is the requirement of an adaptation period which is time consuming.
Furthermore, the ERG responses derived in this manner are recorded under different stimulus conditions, thus, making comparison of these signals difficult. To address these issues and develop a new testing method, we employed silent substitution paradigm in obtaining cone- and rod- isolating ERGs
using sine- and square- wave temporal profiles. The ERGs achieved in this
manner were shown to be photoreceptor-selective. Furthermore, these
responses did not only provide the functional index of photoreceptors but their
contributions to their successive postreceptoral pathways. We believe that the
substitution stimuli used in this thesis could be a valuable tool in functional
assessment of individual photoreceptor classes in normal and pathological conditions. Furthermore, we speculate that this method of cone/rod activity isolation could possibly be used in developing faster and efficient photoreceptor-selective testing protocols without the need of adaptation.Bradford School of Optometry and Vision Sciences scholarshi
Dynamic Data Mining: Methodology and Algorithms
Supervised data stream mining has become an important and challenging data mining task in modern
organizations. The key challenges are threefold: (1) a possibly infinite number of streaming examples
and time-critical analysis constraints; (2) concept drift; and (3) skewed data distributions.
To address these three challenges, this thesis proposes the novel dynamic data mining (DDM)
methodology by effectively applying supervised ensemble models to data stream mining. DDM can be
loosely defined as categorization-organization-selection of supervised ensemble models. It is inspired
by the idea that although the underlying concepts in a data stream are time-varying, their distinctions
can be identified. Therefore, the models trained on the distinct concepts can be dynamically selected in
order to classify incoming examples of similar concepts.
First, following the general paradigm of DDM, we examine the different concept-drifting stream
mining scenarios and propose corresponding effective and efficient data mining algorithms.
• To address concept drift caused merely by changes of variable distributions, which we term
pseudo concept drift, base models built on categorized streaming data are organized and
selected in line with their corresponding variable distribution characteristics.
• To address concept drift caused by changes of variable and class joint distributions, which we
term true concept drift, an effective data categorization scheme is introduced. A group of
working models is dynamically organized and selected for reacting to the drifting concept.
Secondly, we introduce an integration stream mining framework, enabling the paradigm advocated by
DDM to be widely applicable for other stream mining problems. Therefore, we are able to introduce
easily six effective algorithms for mining data streams with skewed class distributions.
In addition, we also introduce a new ensemble model approach for batch learning, following the same
methodology. Both theoretical and empirical studies demonstrate its effectiveness.
Future work would be targeted at improving the effectiveness and efficiency of the proposed
algorithms. Meantime, we would explore the possibilities of using the integration framework to solve
other open stream mining research problems
Systems Analytics and Integration of Big Omics Data
A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome
Genomic and Cellular Studies Establish the Pathogenesis and Cellular Mechanisms of Disease-Causing Mutations in Families with Autosomal Recessive Disorders
The majority of the reported genetic disorders in the UAE population are of the autosomal recessive type, which is mainly due to high rates of consanguinity within the UAE national population, and within a significant proportion of other UAE expatriate communities; such as Arabs and Pakistanis. It is estimated that more than 50% of all marriages among Emiratis occur between biologically related couples, with first cousin marriages being the highest. That could be attributed to sociocultural values in the region. Successful management of genetic diseases can be achieved by the implementation of effective preventative programs that could help reduce the number of new cases, and provide early diagnosis to potentially improve disease management. For these desired outcomes to be achieved, it is imperative to identify the molecular causes (i.e. disease-causing genes and mutations) of such disorders. Therefore, the aim of this study is to elucidate the molecular pathology and cellular mechanisms of a group of recessive disorders affecting Emirati and expatriate families in the UAE. Whole exome sequencing, together with homozygosity mapping and segregation analyses, were performed on the recruited families to elucidate the causative genes and mutations. Where necessary, bioinformatics in silico analyses coupled with cellular and other functional studies were performed to confirm pathogenicity and uncover the cellular mechanisms of the studied disease phenotypes. In this dissertation, I report the identification of two novel compound heterozygous mutations in Multiple PDZ domain (MPDZ) gene causing congenital hydrocephalus, and provide experimental evidence on their pathogenesis and mechanisms of action. In addition, I report the identification of a novel mutation in Xylosyltransferase I (XYLT1) gene responsible for Desbuquois dysplasia II (DBQDII), and provide evidence on the involvement of the endoplasmic reticulum (ER) quality control in the cellular mechanism of several DBQDII-causing mutations, including, the newly identified one. Furthermore, I provide preliminary data on candidate genes in two families affected by suspected monogenic intellectual disability syndromes. Overall, this dissertation provides evidence on the pathogenicity of several mutations and associated cellular mechanisms. The outcomes of this project will likely be valuable for implementing effective preventive strategies at least for the extended family members of the affected individuals
On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator
Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise